EduEVAL-DB: A Role-Based Dataset for Pedagogical Risk Evaluation in Educational Explanations
Javier Irigoyen, Roberto Daza, Aythami Morales, Julian Fierrez, Francisco Jurado, Alvaro Ortigosa, Ruben Tolosana

TL;DR
EduEVAL-DB is a novel dataset featuring teacher role-based explanations and risk annotations, designed to improve automatic pedagogical evaluation and AI tutoring systems across K-12 subjects.
Contribution
This paper introduces EduEVAL-DB, a comprehensive dataset with expert-annotated pedagogical explanations and risk labels, supporting development of pedagogical risk detection models.
Findings
Benchmarking shows Gemini 2.5 Pro outperforms Llama 3.1 8B in pedagogical risk detection.
Supervised fine-tuning on EduEVAL-DB enhances model performance on risk assessment.
The dataset effectively supports evaluation of AI explanations in educational contexts.
Abstract
This work introduces EduEVAL-DB, a dataset based on teacher roles designed to support the evaluation and training of automatic pedagogical evaluators and AI tutors for instructional explanations. The dataset comprises 854 explanations corresponding to 139 questions from a curated subset of the ScienceQA benchmark, spanning science, language, and social science across K-12 grade levels. For each question, one human-teacher explanation is provided and six are generated by LLM-simulated teacher roles. These roles are inspired by instructional styles and shortcomings observed in real educational practice and are instantiated via prompt engineering. We further propose a pedagogical risk rubric aligned with established educational standards, operationalizing five complementary risk dimensions: factual correctness, explanatory depth and completeness, focus and relevance, student-level…
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Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · Intelligent Tutoring Systems and Adaptive Learning · Online Learning and Analytics
